Imagine you are building a model to predict house prices, and your dataset contains a "Zip Code" column. In the United States alone, there are over 40,000 un...
You have cleaned your data, handled missing values, and you are ready to train your first model. You run and immediately hit a brick wall: .
Imagine building a predictive model for a bank loan system. You have income data for 90% of applicants, but for the other 10%, the field is empty. If you sim...
You can have the most sophisticated algorithm in the world—a deep neural network with millions of parameters—but if you feed the network raw, unprocessed gar...
